{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第三步：重新调整弱学习器数目"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#input data\n",
    "dpath = './data/'\n",
    "train = pd.read_csv(dpath +\"RentListingInquries_FE_train.csv\")\n",
    "test = pd.read_csv(dpath +\"RentListingInquries_FE_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train = train['interest_level']\n",
    "train = train.drop([\"interest_level\"], axis=1)\n",
    "X_train = np.array(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# prepare cross validation\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 再次调整弱分类器数目"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def modelfit(alg, X_train, y_train, useTrainCV=True, cv_folds=None, early_stopping_rounds=100):\n",
    "    \n",
    "    if useTrainCV:\n",
    "        xgb_param = alg.get_xgb_params()\n",
    "        xgb_param['num_class'] = 9\n",
    "        \n",
    "        xgtrain = xgb.DMatrix(X_train, label = y_train)\n",
    "        \n",
    "        cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], folds =cv_folds,\n",
    "                         metrics='mlogloss', early_stopping_rounds=early_stopping_rounds)\n",
    "        \n",
    "        n_estimators = cvresult.shape[0]\n",
    "        alg.set_params(n_estimators = n_estimators)\n",
    "        \n",
    "        print (cvresult)\n",
    "        #result = pd.DataFrame(cvresult)   #cv缺省返回结果为DataFrame\n",
    "        #result.to_csv('my_preds.csv', index_label = 'n_estimators')\n",
    "        cvresult.to_csv('my_preds4_2_3_261.csv', index_label = 'n_estimators')\n",
    "        \n",
    "        # plot\n",
    "        test_means = cvresult['test-mlogloss-mean']\n",
    "        test_stds = cvresult['test-mlogloss-std'] \n",
    "        \n",
    "        train_means = cvresult['train-mlogloss-mean']\n",
    "        train_stds = cvresult['train-mlogloss-std'] \n",
    "\n",
    "        x_axis = range(0, n_estimators)\n",
    "        pyplot.errorbar(x_axis, test_means, yerr=test_stds ,label='Test')\n",
    "        pyplot.errorbar(x_axis, train_means, yerr=train_stds ,label='Train')\n",
    "        pyplot.title(\"XGBoost n_estimators vs Log Loss\")\n",
    "        pyplot.xlabel( 'n_estimators' )\n",
    "        pyplot.ylabel( 'Log Loss' )\n",
    "        pyplot.savefig( 'n_estimators4_2_3_261.png' )\n",
    "    \n",
    "    #Fit the algorithm on the data\n",
    "    alg.fit(X_train, y_train, eval_metric='mlogloss')\n",
    "        \n",
    "    #Predict training set:\n",
    "    train_predprob = alg.predict_proba(X_train)\n",
    "    logloss = log_loss(y_train, train_predprob)\n",
    "\n",
    "        \n",
    "    #Print model report:\n",
    "    print (\"logloss of train :\", logloss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     test-mlogloss-mean  test-mlogloss-std  train-mlogloss-mean  \\\n",
      "0              1.957133           0.000806             1.955564   \n",
      "1              1.785371           0.000738             1.782810   \n",
      "2              1.649457           0.000877             1.645860   \n",
      "3              1.537327           0.000630             1.532956   \n",
      "4              1.443652           0.000704             1.438471   \n",
      "5              1.363638           0.000796             1.357667   \n",
      "6              1.294293           0.001085             1.287640   \n",
      "7              1.232981           0.001594             1.225572   \n",
      "8              1.179006           0.001369             1.170946   \n",
      "9              1.131588           0.001206             1.122793   \n",
      "10             1.088893           0.001598             1.079478   \n",
      "11             1.050902           0.001835             1.040914   \n",
      "12             1.016637           0.001695             1.005948   \n",
      "13             0.985869           0.001625             0.974638   \n",
      "14             0.957703           0.001723             0.945891   \n",
      "15             0.931880           0.001742             0.919463   \n",
      "16             0.908832           0.001927             0.895907   \n",
      "17             0.887379           0.002267             0.873983   \n",
      "18             0.868349           0.002371             0.854222   \n",
      "19             0.850711           0.002408             0.835976   \n",
      "20             0.834507           0.002194             0.819263   \n",
      "21             0.819644           0.002193             0.803943   \n",
      "22             0.805978           0.002267             0.789811   \n",
      "23             0.793243           0.002420             0.776509   \n",
      "24             0.781688           0.002520             0.764411   \n",
      "25             0.770958           0.002569             0.753116   \n",
      "26             0.761290           0.002604             0.743006   \n",
      "27             0.751867           0.002462             0.733161   \n",
      "28             0.743439           0.002356             0.724189   \n",
      "29             0.735403           0.002307             0.715724   \n",
      "..                  ...                ...                  ...   \n",
      "231            0.592640           0.002779             0.496903   \n",
      "232            0.592600           0.002759             0.496520   \n",
      "233            0.592556           0.002832             0.496188   \n",
      "234            0.592536           0.002818             0.495802   \n",
      "235            0.592439           0.002840             0.495503   \n",
      "236            0.592489           0.002768             0.495215   \n",
      "237            0.592489           0.002742             0.494903   \n",
      "238            0.592570           0.002697             0.494556   \n",
      "239            0.592550           0.002628             0.494198   \n",
      "240            0.592512           0.002619             0.493845   \n",
      "241            0.592448           0.002560             0.493494   \n",
      "242            0.592495           0.002538             0.493088   \n",
      "243            0.592484           0.002461             0.492752   \n",
      "244            0.592437           0.002456             0.492473   \n",
      "245            0.592441           0.002523             0.492138   \n",
      "246            0.592387           0.002481             0.491829   \n",
      "247            0.592392           0.002438             0.491461   \n",
      "248            0.592386           0.002416             0.491183   \n",
      "249            0.592327           0.002422             0.490847   \n",
      "250            0.592271           0.002408             0.490499   \n",
      "251            0.592219           0.002351             0.490203   \n",
      "252            0.592201           0.002343             0.489855   \n",
      "253            0.592275           0.002328             0.489567   \n",
      "254            0.592302           0.002308             0.489270   \n",
      "255            0.592350           0.002327             0.488964   \n",
      "256            0.592351           0.002347             0.488727   \n",
      "257            0.592286           0.002395             0.488331   \n",
      "258            0.592213           0.002340             0.487989   \n",
      "259            0.592138           0.002422             0.487649   \n",
      "260            0.592183           0.002366             0.487297   \n",
      "\n",
      "     train-mlogloss-std  \n",
      "0              0.001335  \n",
      "1              0.001322  \n",
      "2              0.001608  \n",
      "3              0.001598  \n",
      "4              0.001036  \n",
      "5              0.001235  \n",
      "6              0.001137  \n",
      "7              0.001375  \n",
      "8              0.001241  \n",
      "9              0.001086  \n",
      "10             0.000844  \n",
      "11             0.000733  \n",
      "12             0.000848  \n",
      "13             0.000990  \n",
      "14             0.001057  \n",
      "15             0.001098  \n",
      "16             0.000886  \n",
      "17             0.000654  \n",
      "18             0.000639  \n",
      "19             0.000680  \n",
      "20             0.000902  \n",
      "21             0.000854  \n",
      "22             0.000853  \n",
      "23             0.000680  \n",
      "24             0.000555  \n",
      "25             0.000624  \n",
      "26             0.000723  \n",
      "27             0.000913  \n",
      "28             0.001159  \n",
      "29             0.001195  \n",
      "..                  ...  \n",
      "231            0.001164  \n",
      "232            0.001152  \n",
      "233            0.001211  \n",
      "234            0.001239  \n",
      "235            0.001236  \n",
      "236            0.001221  \n",
      "237            0.001139  \n",
      "238            0.001111  \n",
      "239            0.001138  \n",
      "240            0.001158  \n",
      "241            0.001214  \n",
      "242            0.001201  \n",
      "243            0.001227  \n",
      "244            0.001202  \n",
      "245            0.001274  \n",
      "246            0.001236  \n",
      "247            0.001290  \n",
      "248            0.001221  \n",
      "249            0.001227  \n",
      "250            0.001221  \n",
      "251            0.001270  \n",
      "252            0.001254  \n",
      "253            0.001208  \n",
      "254            0.001228  \n",
      "255            0.001242  \n",
      "256            0.001171  \n",
      "257            0.001185  \n",
      "258            0.001199  \n",
      "259            0.001218  \n",
      "260            0.001224  \n",
      "\n",
      "[261 rows x 4 columns]\n",
      "logloss of train : 0.510360552655\n"
     ]
    },
    {
     "data": {
      "image/png": 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gZmaUJfjU7c/w9V//mV7d9SwieUZJYTwc8ya48HvENz/K4wv+kyuXzuT7D7/I\nqV+6n6aO7qijExHpo6QwXpYshwu/R+ylh/lS2z/zlbcvpLG9m9O//AAv7G6OOjoREUBJYXydfDlc\n+D146RGWP/tBbr/yaHp6nbd861F+8OgmHU4SkcgpKYy3ky+Hd98CDc9xyv3v5In3z6G2vJgv37OB\nS258jHXbG6OOUEQKmJJCFI49F666G7o7mP6z83nq4ha+9a4lPLOtkbd/93dcd8cz7GnqiDpKESlA\nSgpRmXsK/M0jMOuV2P+8j4t2fYfVnz6Lq89awK1/3MIZX/kN/3zXszQ0d0YdqYgUEN3RHLWeLrj/\nH+GJG2D6MXDRDWwuP4HvPriR25/aRszgr89eyN+cs5DpVaVRRysiE9Rw72hWUsgXmx6GX10LTdvh\n1R+CZdexqdH57oMb+cWftmMGFy2Zy7tPO4LTF0zDLFPP5CIimSkpTEQdTXD/P8DqlRAvgbf/O5y0\nnI1727nsB4+zr6WLXnfKimN89M2LuejkucyoLos6ahGZAJQUJrItj8N9n4Xtq2DmK+EtX4Kj30Bb\nVw93r93Jfz+5lVUvHwDg5CNrefPxM3nL8TM5ur5KexAikpGSwkTnDuvvgF98EHo7YeHr4bV/BwuX\ngRkb9zRz7zO7uPGRF2nt6gVgQV0lrz1mOmcunM4ZC6ZTX61zECISUFKYLHo64Y8r4IF/gkQ3zDgB\nzvwgvOqdUBwcOtrZ2M4Dz+7mgQ17+O0LDSTvgSsrjvGOk+ex5IgpvGpuLYtmVlEc1wVnIoVISWGy\n6emEZ/4HHv8+7F4HsWJ4zd/CksugblFfte7eBOu2N/LES/u54eEXaWwf2LdSVWkRl5w6j+Pn1HB0\nfRVH11dSW1Ey3q0RkXGmpDBZucPm38LjN8Bz9wRl806Hk94Nr/iLoLvuFImE8/L+NtZuO8jabY3c\n9uRWmjt7BtQpihnlxXHOf9Vsjp5RycK6KhbUVzKrpozK0qLxapmI5FBeJAUzOxf4NhAHfujuX81Q\n51LgC4ADT7v7ZYMts+CTQqrmXbD2NljzM2jYABgccTocez4c97YBexCpehPOlv1tbGpoYVNDKy82\ntHD32p20dPaQ6a+hrDjGSfNqmVlTxozq0uC1ppQZ1cHrzJoyqpQ8RPJa5EnBzOLA88CbgW3Ak8By\nd382pc4i4DbgDe5+wMxmuPuewZarpJCBO+xeH+w5/Pku2Pl0UD5tIcw/GxacA/PPguqhHwXa2NbN\ni3tbeHkchA1+AAAN2klEQVRfK7ubOtnT1Mnu5g4efa6Blq4eBvtzKS2KsXhmNVPKi6kpL6KmrDgc\nD4eyImrKg7LKkiLKi+OUlcSoCMfjMV05JZIr+ZAUXg18wd3fGk5fB+DuX0mp83XgeXf/4XCXq6Qw\nDI3b4Ll7YeNv4IX/Aw+uTmL6IlhwdpAg5p99yKGm4XB3mjt72NPUyZ6mDvY0d7I7fN3b0klTezdN\nHT2s39FIZ3ci455HNkawu2gECWZBfRUVJfEgeRTH+8bLS8KhOJxXEqe0KEZpUYzieIySeIySopQh\nnvZaFKMoZsRjRlEsFr4aMSUlmcSGmxRyuc8/F9iaMr0NOCOtzmIAM/s9wSGmL7j7r3MYU2GYMg9O\nf38wJHph11p46bfBuYi1P4dVNwX16hbDnFNgzhKYvSR4SlxJ5aCLNjNqyoqpKSvmmBlVwwqno7uX\npo5umtp7aGzvpqm9m7auXtq7w6Grh/auRP94dy/t3Qnau3pp7+6hrauHp7YcoL27F5wRJZqRMsCM\nviu4kmmiOB5j1pQyiuIWJpTUxBK+xoPy4rTp1HpmRsyCdcTMwvUZZmAMnEeGsmz1k2XJOrFwmpTx\n5Kv1LWdgfQas49D6/csJPpkB748F8WSqPzDWgctIlsUs9f1h/ViGNpK5ft+6wvqkzk9u2/AD6Z8O\n6qQWWspkpvphywdMD/j7SSnLVC89lkzLT58/3nKZFDK1Kv3/uQhYBCwD5gG/NbNXuvvBAQsyuwa4\nBuDII48c+0gns1gc5pwcDK/9MPT2BIeXNj8a3CS36WFYe2tQ12JBoph1ItQfGw7HwdQFEB/9n0pZ\n+Et/RvXYNCmpuzdBR3cv7V29dPYk6OpN0NWToDt87epJ0Jky3lfem6A34fQmnO5epzeRoCec7ntN\nK89aLxGW9zpPb22krWvgSXwHSuIx3IN40yX3TtKfpRGz4L0T7DoQybHZU8r4w3VvzOk6cpkUtgFH\npEzPA3ZkqPO4u3cDL5nZcwRJ4snUSu6+AlgBweGjnEVcCOJFMO/UYEhq2gk718CONcHry4/BM7f1\nz48Vw/Sjgw77ps4PhwXBa+0RUBTNTXLF8eBwUXVZcSTrj4q74+EeU6JvPHz1sCxlHhnKPJweUD9M\nTIlMy097f9+y08vS40v0vy+Ioz/W9PqJcD1BGClleN88H/D+gWWJsP3JccLlQ39yTR4uT024/XX6\nv1r653na9MD5meqkb6tDl0mGssHrJQuXzp926ErGWC6TwpPAIjNbAGwH3g2kX1n0S2A5sNLM6ggO\nJ23KYUySSc3sYDj2vP6yzmbY+zw0PA8Nfw7G974AGx+AntRnPRjUzElJFuFQexRMmQtVMyFeWF/a\nuZY8BAMQz7hDLjJ6OUsK7t5jZtcC9xGcL7jJ3deb2ReBVe5+ZzjvLWb2LNALfMLd9+UqJhmB0mqY\ne2owpEokoHUPHNh86PDiQ9CcvjNoUFkH1bPDYVbm18q64FCXiERKN6/J2OrugINbgiTRvCO4l6J5\nZ/DaFE63NnDI6SWLB1dDVdZBRR1UTA/Hp/cPfdN1UD71sM5ziBSafLj6SApRcRnULw6GbHq7oWVP\nSsLY2Z84WvdC2z448BK07YfOpiwLMSiv7U8SFdOhMjk+DcpqoWxKUCd1vKQ6uFRGRDJSUpDxFy8O\nzjdMmTt03Z7OIDm07YO2MGG07kub3hvsmWxfFUwnerIvz2JQWnNosiirDV+npI1PDV9rgvcV6/kV\nMrkpKUh+KyrtPxE+HO7B3kVHI7QfDF47Dg4+3ryrf7x3iGdix0uC5FBWE5x3KamG0qrg/o6SqrCs\nKqUsOb+q/zU5XlypvRbJO0oKMrmYhb/sp0DtKO5p6e5ISRzJ5BEOnU3B0/H6XpuhqyU4V9LVGox3\ntkB36/DXV5KaUKqUZCRySgoiqYrLoHjWsPqJyiqRCBJEX6Jo7k8YXa3Q1RyOJ8uag/JkWdOO/vfn\nKsmUVKbNT5alzC+p1BVhBUhJQWSsxWLB4aWymrFZXqK3P8F0taYlmWTSaR2YZDpTklIyySTLRpJk\n4qVQXB4kiOIKKKkI9khKKoLpvrKKlDrha9b3VQbzissz9xUhkVJSEMl3sXjukkymPZXOZuhug662\nIIF0tYXTrcFrd3tw8r9728A6Pe0jDMQyJ5Whkkl6YhqQjMr7x3XT5KgoKYgUmrFOMkmJRJAYsiaT\ntkPLknW72weWNW3vr5ssS3QPHcOAdhanJZDylL2UipSy9L2etLK+RJS+jMm5p6OkICJjIxbrPxdB\n/dgvv7d76GSSmowOKWsPx1uCGyi7wvcnywe7lDmbTIkidY9lwJ5PeX95UVk4r+zQ6fR58ZJxTT5K\nCiIyMcSLg/tHymtzs/ze7oF7M8lDZQPK2g/d60kt6w73lJKH11LLutsYXcfv1p8kzvwgnPOJsW75\nAEoKIiIQJJ14eDlzLrgHnUl2t/e/9o23BZdDd7elzWsfWK/+uNzElkJJQURkPJj1H0LKY7rTRURE\n+igpiIhIHyUFERHpo6QgIiJ9lBRERKSPkoKIiPRRUhARkT5KCiIi0sfcR3PbdXTMrAF4eZRvrwP2\njmE4+axQ2loo7QS1dTIaz3Ye5e5Ddko14ZLC4TCzVe6+NOo4xkOhtLVQ2glq62SUj+3U4SMREemj\npCAiIn0KLSmsiDqAcVQobS2UdoLaOhnlXTsL6pyCiIgMrtD2FEREZBBKCiIi0qdgkoKZnWtmz5nZ\nRjP7dNTxjCUz22xmz5jZGjNbFZZNM7P7zeyF8HVq1HGOhpndZGZ7zGxdSlnGtlngO+E2Xmtmp0QX\n+chlaesXzGx7uG3XmNn5KfOuC9v6nJm9NZqoR87MjjCzh8xsg5mtN7O/C8sn1XYdpJ35vU3dfdIP\nQBx4EVgIlABPA8dHHdcYtm8zUJdW9nXg0+H4p4GvRR3nKNt2DnAKsG6otgHnA/cCBpwJPBF1/GPQ\n1i8AH89Q9/jw77gUWBD+fcejbsMw2zkbOCUcrwaeD9szqbbrIO3M621aKHsKpwMb3X2Tu3cBtwIX\nRhxTrl0I/Fc4/l/ARRHGMmru/iiwP604W9suBH7kgceBWjObPT6RHr4sbc3mQuBWd+9095eAjQR/\n53nP3Xe6+1PheDOwAZjLJNuug7Qzm7zYpoWSFOYCW1OmtzH4xploHPg/M1ttZteEZTPdfScEf5zA\njMiiG3vZ2jZZt/O14WGTm1IOA06KtprZfOBk4Akm8XZNayfk8TYtlKRgGcom07W4r3X3U4DzgA+Z\n2TlRBxSRybidbwCOBpYAO4FvhOUTvq1mVgXcDnzE3ZsGq5qhbMK0NUM783qbFkpS2AYckTI9D9gR\nUSxjzt13hK97gF8Q7HLuTu5ih697ootwzGVr26Tbzu6+29173T0B/ID+wwkTuq1mVkzwRflTd78j\nLJ502zVTO/N9mxZKUngSWGRmC8ysBHg3cGfEMY0JM6s0s+rkOPAWYB1B+94bVnsv8KtoIsyJbG27\nE3hPeLXKmUBj8nDERJV27PwdBNsWgra+28xKzWwBsAj443jHNxpmZsB/Ahvc/ZspsybVds3Wzrzf\nplGfoR+vgeAKhucJzuh/Nup4xrBdCwmuWHgaWJ9sGzAd+A3wQvg6LepYR9m+Wwh2sbsJfkldna1t\nBLvf3wu38TPA0qjjH4O2/jhsy1qCL43ZKfU/G7b1OeC8qOMfQTvPIjgsshZYEw7nT7btOkg783qb\nqpsLERHpUyiHj0REZBiUFEREpI+SgoiI9FFSEBGRPkoKIiLSR0lBRET6KCmIDIOZLUnr4viCseqC\n3cw+YmYVY7EskcOl+xREhsHMriK4aeraHCx7c7jsvSN4T9zde8c6FhHtKcikYmbzw4ea/CB8sMn/\nmVl5lrpHm9mvw95lf2tmx4Xl7zSzdWb2tJk9GnaN8kXgXeFDUd5lZleZ2fVh/ZVmdkP4QJVNZva6\nsPfLDWa2MmV9N5jZqjCufwrLPgzMAR4ys4fCsuUWPDRpnZl9LeX9LWb2RTN7Ani1mX3VzJ4Ne9v8\nt9x8olJwor4VXIOGsRyA+UAPsCScvg24Ikvd3wCLwvEzgAfD8WeAueF4bfh6FXB9ynv7poGVBM/o\nMII+8ZuAVxH86FqdEkuy24Y48DBwYji9mfAhSQQJYgtQDxQBDwIXhfMcuDS5LIKuECw1Tg0aDnfQ\nnoJMRi+5+5pwfDVBohgg7M74NcDPzWwN8B8ET8oC+D2w0szeT/AFPhz/6+5OkFB2u/szHvSCuT5l\n/Zea2VPAn4ATCJ60le404GF3b3D3HuCnBE9kA+gl6HETgsTTAfzQzC4G2oYZp8igiqIOQCQHOlPG\ne4FMh49iwEF3X5I+w90/YGZnAG8D1pjZIXUGWWcibf0JoCjs9fLjwGnufiA8rFSWYTmZ+tRP6vDw\nPIK795jZ6cAbCXr9vRZ4wzDiFBmU9hSkIHnwsJOXzOyd0Pdw+JPC8aPd/Ql3/0dgL0Ef980Ez9kd\nrRqgFWg0s5kED0RKSl32E8DrzKzOzOLAcuCR9IWFezpT3P0e4CMED2wROWzaU5BCdjlwg5l9Digm\nOC/wNPCvZraI4Ff7b8KyLcCnw0NNXxnpitz9aTP7E8HhpE0Eh6iSVgD3mtlOd3+9mV0HPBSu/x53\nz/QsjGrgV2ZWFtb7+5HGJJKJLkkVEZE+OnwkIiJ9dPhIJj0z+x7w2rTib7v7zVHEI5LPdPhIRET6\n6PCRiIj0UVIQEZE+SgoiItJHSUFERPr8f+FViKEaCzt7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d76cfb7668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#调整max_depth和min_child_weight之后再次调整n_estimators(5,1)\n",
    "xgb2_3 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=261,  #数值大没关系，cv会自动返回合适的n_estimators\n",
    "        max_depth=5,\n",
    "        min_child_weight=1,\n",
    "        gamma=0,\n",
    "        subsample=0.3,\n",
    "        colsample_bytree=0.8,\n",
    "        colsample_bylevel=0.7,\n",
    "        objective= 'multi:softprob',\n",
    "        seed=3)\n",
    "\n",
    "modelfit(xgb2_3, X_train, y_train, cv_folds = kfold)\n",
    "#from sklearn.model_selection import cross_val_score\n",
    "#results = cross_val_score(xgb2_3, X_train, y_train, metrics='mlogloss', cv=kfold)\n",
    "#print results\n",
    "#print(\"CV logloss: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jn+/eVFPe5ZvXs07VVPk/vO4ZJq4+itVHVwJb1+O2tHX4JU/qZz0Fr+U1HFKS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\nkzr+ET1wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d76e9d1e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cvresult = pd.DataFrame.from_csv('my_preds4_2_3_261.csv')\n",
    "\n",
    "cvresult = cvresult.iloc[100:]\n",
    "# plot\n",
    "test_means = cvresult['test-mlogloss-mean']\n",
    "test_stds = cvresult['test-mlogloss-std'] \n",
    "        \n",
    "train_means = cvresult['train-mlogloss-mean']\n",
    "train_stds = cvresult['train-mlogloss-std'] \n",
    "\n",
    "x_axis = range(100,cvresult.shape[0]+100)\n",
    "        \n",
    "fig = pyplot.figure(figsize=(10, 10), dpi=100)\n",
    "pyplot.errorbar(x_axis, test_means, yerr=test_stds ,label='Test')\n",
    "pyplot.errorbar(x_axis, train_means, yerr=train_stds ,label='Train')\n",
    "pyplot.title(\"XGBoost n_estimators vs Log Loss\")\n",
    "pyplot.xlabel( 'n_estimators' )\n",
    "pyplot.ylabel( 'Log Loss' )\n",
    "pyplot.savefig( 'n_estimators_detail4_2_3_699.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 第二次调整弱分类器的最优数目为260"
   ]
  }
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